Abstract
Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee’s performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers, such as self-appraisal, supervisor assessment and peer feedback. Analysis of the structured data and text produced in PA is crucial for measuring the quality of appraisals and tracking actual improvements. In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries. All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.
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Notes
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We used 100 dimensional word vectors trained on Wikipedia 2014 and Gigaword 5 corpus, available at: https://nlp.stanford.edu/projects/glove/.
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We used the default parameter settings for Carrot2 Lingo algorithm as mentioned at: http://download.carrot2.org/head/manual/index.html.
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Palshikar, G.K., Pawar, S., Chourasia, S., Ramrakhiyani, N. (2018). Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_47
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